Fraud is prevalent in many high-volume areas, such as on-line shopping, telecommunications, banking, social security claims, etc., where a manual review of all transactions is not possible and decisions must be made quickly to prevent crime. Fraud is increasing with the expansion of computing technology and globalization, with criminals devising new frauds to overcome the strategies already in place to stop them. Fraud detection can be automated through the through the use of a Fraud Management System (FMS). In various embodiments, an FMS can comprise a rule-based expert system that stores and uses knowledge in a transparent way that is straightforward for a fraud expert to modify and interpret.
Classifiers, such as neural networks, can be trained to recognize fraudulent transactions, such as transactions relating to payment cards, for example, credit and debit cards, gift cards, top-up cards, fuel cards, and various types of pre-paid cards. A classifier trained to recognize fraudulent transactions contains information about patterns in fraudulent transactions that may be helpful to human experts in understanding payment card fraud. Such information can be extracted as human readable rules using various techniques. One such technique is a decompositional approach, where rules are created using heuristic searches that guide the process of rule extraction. This method works by decomposing the classifier architecture and therefore produces rules that represent the internal architecture. Another such technique is a pedagogical approach, such as disclosed below, where a set of global rules are extracted from the classifier in terms of the relationships between only the inputs and the outputs. These individual rules are then combined into a set of rules that describes the classifier as a whole.